Manufacturing present day integrated circuits is a long, complex, and expensive process. A state-of-the art integrated circuit requires between 200-300 processing steps, each of which must satisfy a tight set of specifications. Since equipment malfunctions are inevitable, it becomes essential for profitable manufacture of integrated circuits that the equipment malfunctions be quickly identified and repaired. This invention describes techniques for rapid identification of the causes of equipment malfunctions.
As an example of the problem addressed by this invention, FIG. 1 represents a plasma enhanced chemical vapor deposition process. The process outputs of interest are the film thickness of the deposited films, film refractive index, stress on the wafer due to the deposited film, and the film nonuniformity. The process inputs manipulated to get the desired values of the outputs are, say, three gases g.sub.1, g.sub.2, g.sub.3 ; radio frequency (RF) power used to create the plasma; and the pressure in the vacuum chamber. Suppose that due to a miscalibration in one of the gas delivery systems the delivered gas flow is different from the requested flow. This could result in one or more process outputs being different from the desired values. Since the efficacy of the future processing steps depends on previous steps, and the functionality of the integrated circuit relies on each set performing to specifications, one would like to quickly identify the miscalibrated gas flow and correct it before it prevents a large number of integrated circuits from being correctly manufactured.
The diagnosis techniques described in this invention make use of equipment models for fault isolation. Equipment models describe relationships between process inputs and outputs. Equipment models can be obtained by two main techniques. The first is by modeling the underlying physics of the process, resulting in physically based models. The second technique ignores the underlying physics but models the process implemented by the equipment as a "black box" by fitting a predetermined functional form to process outputs (responses) at carefully selected inputs. Such models are called response surface models (RSM). See Box et al. book entitled Empirical Model-Building and Response Surfaces, published by John Wiley & Sons, New York, 1987. The diagnostic techniques described in this invention have been tested on RSM models, but could in principle be applied to physically based models also.
May and Spanos, in their article entitled Automated Malfunction Diagnosis of Semiconductor Fabrication Equipment: A Plasma Etch Application in "IEEE Transactions on Semiconductor Manufacturing", (6)1:28-40, 1993, report the use of RSMs for diagnosis. Their approach is based on the analysis of residuals that result from substituting the input parameter value that best explains a set of observations in the least square sense. In their approach, one needs to linearize the models around the current operating point. This limits the applicability of their approach for diagnosing large fault magnitudes using non-linear models. Furthermore, their approach also requires having models of multiple output of the process. B.T.B. Chu describes an approach using RSMs for diagnosis in Fault Diagnosis With Continuous System Models, in "IEEE Transactions on Systems, Man, and Cybernetics", (23)1:55-64, 1993. In Chu's approach for using RSMs for diagnosis, one discretizes the outputs and determines the probability of observing an output for different input combinations. On observing the output, one searches for the input that will maximize the probability of observing that value. The process of computing the probabilities can be computationally quite expensive. Furthermore, the outputs have to be discretized.